Package: geostatsp 2.2.1

Patrick Brown

geostatsp: Geostatistical Modelling with Likelihood and Bayes

Geostatistical modelling facilities using 'SpatRaster' and 'SpatVector' objects are provided. Non-Gaussian models are fit using 'INLA', and Gaussian geostatistical models use Maximum Likelihood Estimation. For details see Brown (2015) <doi:10.18637/jss.v063.i12>. The 'RandomFields' package is available at <https://web.archive.org/web/20250719184025/https://www.wim.uni-mannheim.de/schlather/publications/software> and <https://github.com/cran/RandomFields>.

Authors:Patrick Brown [aut, cre, cph]

geostatsp_2.2.1.tar.gz
geostatsp_2.2.1.zip(r-4.7-x86_64)geostatsp_2.2.1.zip(r-4.7-arm64)geostatsp_2.2.1.zip(r-4.6-x86_64)geostatsp_2.2.1.zip(r-4.6-arm64)geostatsp_2.2.1.zip(r-4.5-x86_64)
geostatsp_2.2.1.tgz(r-4.6-x86_64)geostatsp_2.2.1.tgz(r-4.6-arm64)geostatsp_2.2.1.tgz(r-4.5-x86_64)geostatsp_2.2.1.tgz(r-4.5-arm64)
geostatsp_2.2.1.tar.gz(r-4.7-arm64)geostatsp_2.2.1.tar.gz(r-4.7-x86_64)geostatsp_2.2.1.tar.gz(r-4.6-arm64)geostatsp_2.2.1.tar.gz(r-4.6-x86_64)
geostatsp_2.2.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
geostatsp/json (API)

# Install 'geostatsp' in R:
install.packages('geostatsp', repos = c('https://r-forge.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://r-forge.r-project.org/projects/diseasemapping

Uses libs:
  • openblas– Optimized BLAS
Datasets:

On CRAN:

Conda:

openblas

5.04 score 68 scripts 637 downloads 30 exports 6 dependencies

Last updated from:5aaec40632. Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK283
linux-devel-x86_64OK386
source / vignettesOK407
linux-release-arm64OK416
linux-release-x86_64OK496
macos-release-arm64OK326
macos-release-x86_64OK587
macos-oldrel-arm64OK372
macos-oldrel-x86_64OK856
windows-devel-arm64OK442
windows-devel-x86_64OK444
windows-release-arm64OK463
windows-release-x86_64OK528
windows-oldrel-x86_64OK350
wasm-releaseOK301

Exports:conditionalGmrfexcProbfillParamgeostatDataglgminformationLgminla.modelsinlaAvailableinlaSetThreadskrigeLgmlgcplgmlikfitLgmloglikLgmmaternmaternGmrfPrecmodelRandomFieldsNNmatpcPriorRangepostExpprofLlgmRFsimulatesimLgcpsimPoissonPPspatialRocspdfToBricksquareRasterstackRasterListvariogvariogMcEnv

Dependencies:abindlatticeMatrixnumDerivRcppterra

LGCP with PC priors
Rendered fromlgcp.Rnwusingknitr::knitr

Last update: 2026-08-05
Started: 2016-02-18

Various GLGM examples
Rendered fromglgm.Rnwusingknitr::knitr

Last update: 2026-08-05
Started: 2017-07-26

Readme and manuals

Help Manual

Help pageTopics
Conditional distribution of GMRFconditionalGmrf
Exceedance probabilitiesexcProb
Gambia datagambiaUTM
Prepare observations and covariates for a geostatistical modelgeostatData geostatData.default geostatData.SpatRaster geostatData.SpatVector
Generalized Linear Geostatistical Modelsglgm glgm,ANY,ANY,ANY,ANY-method glgm,formula,data.frame,SpatRaster,data.frame-method glgm,formula,SpatRaster,ANY,ANY-method glgm,formula,SpatVector,ANY,ANY-method glgm-methods lgcp
Valid models in INLAinla.models
Check whether INLA is usableinlaAvailable inlaSetThreads
Spatial prediction, or KrigingkrigeLgm
Linear Geostatistical Modelslgm lgm,character,ANY,ANY,ANY-method lgm,formula,data.frame,SpatRaster,data.frame-method lgm,formula,SpatRaster,ANY,ANY-method lgm,formula,SpatVector,numeric,ANY-method lgm,formula,SpatVector,SpatRaster,data.frame-method lgm,formula,SpatVector,SpatRaster,list-method lgm,formula,SpatVector,SpatRaster,missing-method lgm,formula,SpatVector,SpatRaster,SpatRaster-method lgm,missing,ANY,ANY,ANY-method lgm,numeric,ANY,ANY,ANY-method lgm-methods
Likelihood Based Parameter Estimation for Gaussian Random FieldslikfitLgm loglikLgm
Loaloa prevalence data from 197 village surveyselevationLoa eviLoa loaloa ltLoa tempLoa
Evaluate the Matern correlation functionfillParam matern matern.default matern.dist matern.SpatRaster matern.SpatVector
Precision matrix for a Matern spatial correlationmaternGmrfPrec maternGmrfPrec.default maternGmrfPrec.dgCMatrix NNmat NNmat.default NNmat.SpatRaster
Murder locationsmurder torontoBorder torontoIncome torontoNight torontoPdens
PC prior for range parameterpcPrior pcPriorRange
Exponentiate posterior quantilespostExp
Joint confidence regionsinformationLgm profLlgm
Simulation of Random FieldsmodelRandomFields RFsimulate RFsimulate,ANY,SpatRaster-method RFsimulate,data.frame,ANY-method RFsimulate,matrix,SpatRaster-method RFsimulate,matrix,SpatVector-method RFsimulate,numeric,SpatRaster-method RFsimulate,numeric,SpatVector-method RFsimulate,RMmodel,SpatRaster-method RFsimulate,RMmodel,SpatVector-method RFsimulate-methods
Rongelap datarongelapUTM
Simulate a log-Gaussian Cox processsimLgcp simPoissonPP
Sensitivity and specificityspatialRoc
Create a raster with square cellssquareRaster squareRaster,matrix-method squareRaster,SpatExtent-method squareRaster,SpatRaster-method squareRaster,SpatVector-method squareRaster-methods
Converts a list of SpatRasters, possibly with different projections and resolutions, to a single multi-layer SpatRaster.spdfToBrick stackRasterList
Swiss rainfall dataswissAltitude swissBorder swissLandType swissRain
Raster of Swiss rain dataswissNN swissRainR
Compute Empirical Variograms and Permutation Envelopesvariog variog.default variog.SpatVector variogMcEnv variogMcEnv.default variogMcEnv.SpatVector
Mercer and Hall wheat yield datawheat